import matplotlib.pyplot as plt
import networkx as nx
import PIL
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.image as mpimg
from matplotlib import transforms, image
from scipy import ndimage

#reading images 
chicago = image.imread('us/chicago.png')
independence = image.imread('us/independence.png')
first_party = image.imread('us/first-party.png')
bakchod1 = mpimg.imread('us/bakchod1.png')
bakchod2 = mpimg.imread('us/bakchod2.png')
bakchod3 = mpimg.imread('us/bakchod3.png')
hallow = mpimg.imread('us/hallow.png')
hallow2 = mpimg.imread('us/hallow2.png')
diwali = mpimg.imread('us/diwali.png')
grouphug = mpimg.imread('us/group-hug.png')
love = mpimg.imread('us/love.png')
ischool = mpimg.imread('us/ishcool.png')
pix = mpimg.imread('us/pix.png')

# Image URLs for graph nodes
icons = {
    "sax": "sax.png",
    "rao": "rao.png",
    "prade": "prade.png",
    "bum":"bum.png",
    "home":"home.png"
}

# Load images
images = {k: PIL.Image.open(fname) for k, fname in icons.items()}

# Generate the computer network graph
G = nx.Graph()


G.add_node("sax", image=images["sax"], pos = (0,10))
G.add_node("rao", image=images["rao"],pos = (10,20))
G.add_node("home", image=images["home"],pos = (10,10))
G.add_node("bum", image=images["bum"],pos = (10,0))
G.add_node("prade", image=images["prade"],pos = (20,10))

G.add_edge("sax", "rao", color='lightgreen')
G.add_edge("sax", "bum", color='yellow')
G.add_edge("home", "sax", color='red')
G.add_edge("home", "rao", color='red')
G.add_edge("home", "prade", color='red')
G.add_edge("home", "bum", color='red')
G.add_edge("rao", "prade", color='pink')
G.add_edge("bum", "prade", color='lightgreen')

edges = G.edges()
colors = [G[u][v]['color'] for u,v in edges]
print(colors)

['lightgreen', 'yellow', 'red', 'red', 'pink', 'red', 'red', 'lightgreen']

# Get a reproducible layout and create figure
def make_graph():
    pos=nx.get_node_attributes(G,'pos')
    fig, ax = plt.subplots(figsize=(10, 10))
    # Note: the min_source/target_margin kwargs only work with FancyArrowPatch objects.
    # Force the use of FancyArrowPatch for edge drawing by setting `arrows=True`,
    # but suppress arrowheads with `arrowstyle="-"`

    nx.draw_networkx_edges(
        G,
        pos=pos,
        ax=ax,
        arrows=True,
        arrowstyle="-",
        min_source_margin=15,
        min_target_margin=15,
        width=5, 
        edge_color=colors,
        style="solid",
        node_shape="o"
    )

    # Transform from data coordinates (scaled between xlim and ylim) to display coordinates
    tr_figure = ax.transData.transform
    # Transform from display to figure coordinates
    tr_axes = fig.transFigure.inverted().transform

    # Select the size of the image (relative to the X axis)
    icon_size = (ax.get_xlim()[1] - ax.get_xlim()[0]) * 0.0025
    icon_center = icon_size / 2.0

    # Add the respective image to each node
    for n in G.nodes:
        xf, yf = tr_figure(pos[n])
        xa, ya = tr_axes((xf, yf))
        # get overlapped axes and plot icon
        a = plt.axes([xa - icon_center, ya - icon_center, icon_size, icon_size])
        a.imshow(G.nodes[n]["image"])
        a.axis("off")
    plt.show()

Vizualizing the story of 305 - Just how you like it!

This is a series of network graphs which will update the strengths of our relationships, eventually turning into the family that we are! Lets start with an inital network graph!

make_graph()

Image Journey PT-1

Let's start with when we were casually filling google forms, bonding on barcelona, discussing visa (HAIL PANKIL!), anime and just getting to know each other! Bakchodi toh day 1 se thi, aise hi thodi $1000 ka amazon coupon le liya tha Michael se ;) Forward to Independence day, Chicago and our first ever party here our first ever picture!

plt.figure(figsize=(12,16))
plt.axis("off")
plt.imshow(ndimage.rotate(independence,270))
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

plt.figure(figsize=(12,16))
plt.axis("off")
plt.imshow(chicago)
plt.show()

plt.figure(figsize=(12,16))
plt.axis("off")
plt.imshow(first_party)
plt.show()

You were the last one to come here but you sure made it a point the Friday Lord bonds us well. Let's look at the updated status of our network graph!

colors[1] = 'lightgreen'
# colors[6] = 'lightgreen'
colors[2] = 'pink'
colors[3] = 'pink'
colors[4] = 'lightgreen'
colors[5] = 'pink'
colors[6] = 'pink'

make_graph()

Festivals TB?!?!

Festivals only bought us closer, we'll let matplotlib help us travel through the memory lane!

plt.figure(figsize=(12,16))
plt.axis("off")
plt.imshow(ndimage.rotate(hallow,270))
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

plt.figure(figsize=(12,16))
plt.axis("off")
plt.imshow(hallow2)
plt.show()

plt.figure(figsize=(12,16))
plt.axis("off")
plt.imshow(diwali)
plt.show()

Network Graph Status Update

Zenitsu, Tanjiro, Kyojiro and Giyu had bonded damn well till then. Many fuck ups and laughs later...

colors[2] = 'lightgreen'
colors[3] = 'lightgreen'
colors[5] = 'lightgreen'
colors[6] = 'lightgreen'

make_graph()

The feeling of Home

Home started to feel what it really should be. Tons of dal-chawal, chamkila, bindi, goba and upsetting barca matches, all started to feel better with the 4 of us around. We sure got a little photogenic but who cares?

ITS 305 represent!

plt.figure(figsize=(12,16))
plt.axis("off")
plt.imshow(ndimage.rotate(grouphug,270))
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

plt.figure(figsize=(12,16))
plt.axis("off")
plt.imshow(ndimage.rotate(love,270))
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

plt.figure(figsize=(12,16))
plt.axis("off")
plt.imshow(ischool)
plt.show()

HOME

home feels complete, so should the network graph right?

for i in range(0, len(colors)):
    colors[i] = 'darkgreen'
make_graph()

Aaditya's Favourite

Knowing that you are a sucker for heatmaps, want to see one tailored just for you?

file = pd.ExcelFile('home305.xlsx')

names = file.sheet_names  # see all sheet names

df = pd.concat([file.parse(name) for name in names])

x_values = [df.x1, df.x2]
y_values = [df.y1, df.y2]

rao_heatmap = np.zeros((140,100))

#updating heat values

#rao room
for x in range (110,140):
    for y in range (60, 100):
        rao_heatmap[x][y] = 0.65

#rao bed
for x in range (120,140):
    for y in range (80,100):
        rao_heatmap[x][y] = 0.75
        
for x in range (125,135):
    for y in range (85,95):
        rao_heatmap[x][y] = 0.85

for x in range (128,132):
    for y in range (88,92):
        rao_heatmap[x][y] = 0.95
        
#hallway
for x in range (30,140):
    for y in range (50,60):
        rao_heatmap[x][y] = 0.35
        
#bathroom

#main
for x in range (90,110):
    for y in range (60,100):
        rao_heatmap[x][y] = 0.45
#pot
for x in range (90,95):
    for y in range (70,75):
        rao_heatmap[x][y] = 0.6
#other       
for x in range (60,80):
    for y in range (60,100):
        rao_heatmap[x][y] = 0.2
#pot
for x in range (75,80):
    for y in range (70,75):
        rao_heatmap[x][y] = 0.35
        
        
#Kitchen
#non-walkable
for x in range (30,60):
    for y in range (60,100):
        rao_heatmap[x][y] = 0.05
        
#walkable
for x in range (42,48):
    for y in range (60,100):
        rao_heatmap[x][y] = 0.4
        
#Hall

for x in range (0,30):
    for y in range (10,100):
        rao_heatmap[x][y] = 0.4
#dining table
for x in range (15,25):
    for y in range (75,95):
        rao_heatmap[x][y] = 0.5
        
#couches
for x in range (0,10):
    for y in range (15,25):
        rao_heatmap[x][y] = 0.65
for x in range (0,10):
    for y in range (35,65):
        rao_heatmap[x][y] = 0.65
        
#balcony
for x in range (10,20):
    for y in range (5,10):
        rao_heatmap[x][y] = 0.3
    
#sax room
for x in range (80,110):
    for y in range (10,50):
        rao_heatmap[x][y] = 0.2

#prade room
for x in range (110,140):
    for y in range (10,50):
        rao_heatmap[x][y] = 0.15  
    
#bum room
for x in range (30,60):
    for y in range (10,50):
        rao_heatmap[x][y] = 0.1

plt.figure(figsize=(12,16))
plt.xlim(-10, 110), plt.ylim(0, 140)
plt.plot(x_values, y_values,'black')
plt.imshow(rao_heatmap, zorder=0, aspect="auto", extent=(0,100,0,140),cmap='hot_r', origin = 'lower')
plt.axis("off")
plt.title("Aaditya Rao's Heatmap for 506 S Fourth - 305",fontsize = 40)
plt.show()

Love, your 305 emo-bois

plt.figure(figsize=(12,16))
plt.axis("off")
plt.imshow(ndimage.rotate(pix,270))
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).